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Conventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 4 Titre : Conventional and neural network-based water vapor density model for GNSS troposphere tomography Type de document : Article/Communication Auteurs : Chen Liu, Auteur ; Yibin Yao, Auteur ; Chaoqian Xu, Auteur Année de publication : 2022 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] classification par réseau neuronal [Termes IGN] erreur absolue [Termes IGN] étalonnage de modèle [Termes IGN] modèle météorologique [Termes IGN] propagation troposphérique [Termes IGN] tomographie par GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) Global navigation satellite system (GNSS) water vapor (WV) tomography is a promising technique to reconstruct the three-dimensional (3D) WV field. However, this technique usually suffers from the ill-posed problem caused by the poor geometry of GNSS rays, resulting in underdetermined tomographic equations. Such equations often rely on iterative methods for solving, but conventional iterative approaches require accurate initial WV density. To address this demand, we proposed two models for WV density estimation. One is the conventional model (CO model) that consists of an exponential model and a linear least-squares model, which are used to describe the spatial and temporal variability of the WV density, respectively. The other is a neural network model (NN model) that uses a backpropagation neural network (BPNN) to fit the nonlinear variation of WV density in both spatial and temporal domains. WV density derived from a Hong Kong (HK) radiosonde station (RS) during 2020 was used to validate the proposed models. Validation results show that both models well describe the spatial and temporal distribution of the WV density. The NN model exhibits better prediction performance than the CO model in terms of root mean square error (RMSE) and bias. We also applied the proposed models to GNSS WV tomography to test their performance in extreme weather conditions. Test results show that the proposed model-based GNSS tomography can correct the content of WV density but cannot accurately sense its irregular distribution. Numéro de notice : A2022-005 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01188-x Date de publication en ligne : 23/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01188-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98920 [article]

Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)  

Public [article]  inAdvances in space research > vol 69 n° 7 (April 2022) . - pp 2671 - 2681 Titre : Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Behzad Voosoghi, Auteur Année de publication : 2022 Article en page(s) : pp 2671 - 2681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] algorithme génétique [Termes IGN] apprentissage automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Inférence floue [Termes IGN] Iran [Termes IGN] précipitation [Termes IGN] radiosondage [Termes IGN] réseau neuronal artificiel [Termes IGN] retard hydrostatique [Termes IGN] retard troposphérique zénithal [Termes IGN] tomographie par GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWDSVM) and ANN (ZWDANN) is compared with the ZWD obtained from the GPS (ZWDGPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWVSVM, PWVANN) is compared with the radiosonde PWV (PWVradiosonde) and voxel-based PWV (PWVVBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations. Numéro de notice : A2022-446 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.asr.2022.01.003 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.asr.2022.01.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100106 [article]

Ionospheric tomographic common clock model of undifferenced uncombined GNSS measurements / German Olivares-Pulido in Journal of geodesy, vol 95 n° 11 (November 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 11 (November 2021) . - n° 122 Titre : Ionospheric tomographic common clock model of undifferenced uncombined GNSS measurements Type de document : Article/Communication Auteurs : German Olivares-Pulido, Auteur ; Manuel Hernández-Pajares, Auteur ; Haixia Lyu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] correction ionosphérique [Termes IGN] horloge du satellite [Termes IGN] mesurage par GNSS [Termes IGN] modèle ionosphérique [Termes IGN] phase [Termes IGN] positionnement ponctuel précis [Termes IGN] teneur totale en électrons [Termes IGN] tomographie par GPS [Termes IGN] voxel [Termes IGN] Wide Area Augmentation System [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) In this manuscript, we introduce the Ionospheric Tomographic Common Clock (ITCC) model of undifferenced uncombined GNSS measurements. It is intended for improving the Wide Area precise positioning in a consistent and simple way in the multi-GNSS context, and without the need of external precise real-time products. This is the case, in particular, of the satellite clocks, which are estimated at the Wide Area GNSS network Central Processing Facility (CPF) referred to the reference receiver one; and the precise realtime ionospheric corrections, simultaneously computed under a voxel-based tomographic model with satellite clocks and other geodetic unknowns, from the uncombined and undifferenced pseudoranges and carrier phase measurements at the CPF from the Wide Area GNSS network area. The model, without fixing the carrier phase ambiguities for the time being (just constraining them by the simultaneous solution of both ionospheric and geometric components of the uncombined GNSS model), has been successfully applied and assessed against previous precise positioning techniques. This has been done by emulating real-time conditions for Wide Area GPS users during 2018 in Poland. Numéro de notice : A2021-776 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01568-8 Date de publication en ligne : 13/10/2021 En ligne : https://doi.org/10.1007/s00190-021-01568-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98839 [article]

Ordered subsets-constrained ART algorithm for ionospheric tomography by combining VTEC data / Dunyong Zheng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 7051 - 7061 Titre : Ordered subsets-constrained ART algorithm for ionospheric tomography by combining VTEC data Type de document : Article/Communication Auteurs : Dunyong Zheng, Auteur ; Yibin Yao, Auteur ; Wenfeng Nie, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7051 - 7061 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] modèle ionosphérique [Termes IGN] teneur totale en électrons [Termes IGN] teneur verticale totale en électrons [Termes IGN] tomographie par GPS Résumé : (auteur) Computerized ionospheric tomography is an important technique for ionosphere investigation. However, it is an ill-posed problem owing to an insufficient amount of available data, because of which the distributions of ionospheric electron density (IED) cannot be reconstructed accurately. In light of this, the ordered subsets-constrained algebraic reconstruction technique (OS_CART) is developed here using vertical total electron content (VTEC) data to solve this problem, where the VTEC derived from the slant total electron content (STEC) of Global Navigation Satellite System (GNSS) signal paths is used to compensate for the lack of data provided by GNSS observations in inversion regions, and the OS_CART is also used to improve the spatial resolution and inversion efficiency. The proposed method was validated by conducting numerical experiments using GNSS and independent ionosonde data in both quiescent and disturbed ionospheric conditions. In contrast to classical methods of ionospheric tomography, the proposed method exhibited significantly higher reconstruction accuracy. While delivering a comparable accuracy to that of traditional methods in terms of self-consistency validation using STEC data and without overfitting, the proposed method yielded a more than 90% improvement over the self-consistency validation using VTEC data. In addition, a better daily description of the ionosphere was obtained using the proposed method, where an increase in the peak height and irregular changes to the IED, associated with variations in the number of epochs and the occurrence of magnetic storms, were observed. Overall, the results reveal that the proposed method is a useful tool for research on space weather. Numéro de notice : A2021-634 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029819 Date de publication en ligne : 28/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98297 [article]

Adaptive regularization method for 3-D GNSS ionospheric tomography based on the U-curve / Jun Tang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4547 - 4560 Titre : Adaptive regularization method for 3-D GNSS ionospheric tomography based on the U-curve Type de document : Article/Communication Auteurs : Jun Tang, Auteur ; Xin Gao, Auteur Année de publication : 2021 Article en page(s) : pp 4547 - 4560 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] modèle ionosphérique [Termes IGN] problème inverse [Termes IGN] teneur totale en électrons [Termes IGN] tomographie par GPS Résumé : (auteur) Computerized ionospheric tomography is a highly ill-posed inverse problem, and regularization tends to stabilize the problem to provide a unique solution. When a regularization method is used, the choice of an optimal parameter is a key issue. In this article, we propose an adaptive regularization method for 3-D ionospheric tomography based on the U-curve. The proposed approach uses a U-curve method to determine the optimal regularization parameter from Global Navigation Satellite Systems (GNSS) observation data. Comparative case studies are investigated based on GNSS simulated observations and real measurements. The simulation results indicate that the proposed method is superior to the adaptive regularization method based on the L-curve. In addition, we further validate the tomographic results with actual ionosonde station data. The results demonstrate the reliability and superiority of the proposed method compared to traditional methods. Numéro de notice : A2021-422 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022561 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3022561 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97777 [article]

Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study / Mir Reza Ghaffari Razin in GPS solutions, Vol 24 n° 3 (July 2020)  

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A 4D tomographic ionospheric model to support PPP-RTK / German Olivares-Pulido in Journal of geodesy, vol 93 n° 9 (September 2019)  

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Analysis of Galileo and GPS integration for GNSS tomography / Pedro Benevides in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Ionospheric tomography based on GNSS observations of the CMONOC: performance in the topside ionosphere / Zhe Yang in GPS solutions, vol 21 n° 2 (April 2017)  

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Ionospheric tomography using GNSS: multiplicative algebraic reconstruction technique applied to the area of Brazil / Fabricio Dos Santos Prol in GPS solutions, vol 20 n° 4 (October 2016)  

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Water vapor tomography using global navigation satellites systems / Donat Perler (2012)  

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Mesoscale GPS tomography applied to the 12 June 2002 convective initiation event of IHOP_2002 / Cédric Champollion in Quarterly Journal of the Royal Meteorological Society, vol 135 n° 640 (April 2009 part A)  

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Quantification de la vapeur d'eau troposphérique par GPS (modèles 2D et tomographies 3D) - Application aux précipitations intenses / Cédric Champollion (2005)

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